Schmalenbach Business Review

, Volume 58, Issue 4, pp 332–348 | Cite as

Estimating the Global Minimum Variance Portfolio

Global Minimum Variance Portfolio

Abstract

According to standard portfolio theory, the tangency portfolio is the only efficient stock portfolio. However, empirical studies show that an investment in the global minimum variance portfolio often yields better out-of-sample results than does an investment in the tangency portfolio and suggest investing in the global minimum variance portfolio. But little is known about the distributions of the weights and return parameters of this portfolio. Our contribution is to determine these distributions. By doing so, we answer several important questions in asset management.

Keywords

Estimation Risk Global Minimum Variance Portfolio Weight Estimation 

JEL-Classification

C22 G11 

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References

  1. Bawa, Vijay S., J. Brown Stephen, and Roger W. Klein (1979), Estimation Risk and Optimal Portfolio Choice, Amsterdam: North Holland.Google Scholar
  2. Chopra, Vijay K. and William T. Ziemba (1993), The Effect of Errors in Means, Variances, and Covariances on Optimal Portfolio Choice, Journal of Portfolio Management 19, 6–11.CrossRefGoogle Scholar
  3. Dickinson, John P. (1974), The Reliability of Estimation Procedures in Portfolio Analysis, Journal of Financial and Quantitative Analysis 9, 447–462.CrossRefGoogle Scholar
  4. Dorfleitner, Gregor (2003), Why the Return Notion Matters, International Journal of Theoretical and Applied Finance 6, 73–88.CrossRefGoogle Scholar
  5. Greene, William H. (2000), Econometric Analysis, 4th Edition, New Jersey: Prentice Hall.Google Scholar
  6. Hayashi, Fumio (2000), Econometrics, Princeton: Princeton University Press.Google Scholar
  7. Huang, Chi-Fu and Robert H. Litzenberger (1988), Foundations for Financial Economics, New Jersey: Prentice Hall.Google Scholar
  8. Ingersoll, Jonathan E. (1987), Theory of Financial Decision Making, Savage: Rowman and Littlefield.Google Scholar
  9. Jagannathan, Ravi and Tongshu Ma (2003), Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps, Journal of Finance 58, 1651–1683.CrossRefGoogle Scholar
  10. Jorion, Philippe (1985), International Portfolio Diversification with Estimation Risk, Journal of Business 58, 259–278.CrossRefGoogle Scholar
  11. Jorion, Philippe (1991), Bayesian and CAPM Estimators of the Means: Implications for Portfolio Selection, Journal of Banking and Finance 15, 717–727.CrossRefGoogle Scholar
  12. Kan, Raymond and Guofu Zhou (2001), Tests of Mean-Variance Spanning, Working Paper, University of Toronto.Google Scholar
  13. Ledoit, Olivier and Michael Wolf (2003), Improved Estimation of the Covariance Matrix of Stock Returns with an Application to Portfolio Selection, Journal of Empirical Finance 10, 603–621.CrossRefGoogle Scholar
  14. Lütkepohl, Helmut (1996), Handbook of Matrices, New York: John Wiley & Sons.Google Scholar
  15. Merton, Robert C. (1980), On Estimating the Expected Return on the Market: An Exploratory Investigation, Journal of Financial Economics 8, 323–361.CrossRefGoogle Scholar
  16. Newey, Withney K. and Kenneth D. West (1987), A Simple Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix, Econometrica 55, 703–708.CrossRefGoogle Scholar
  17. Ohkrin, Yarema, and Wolfgang Schmid (2005), Distributional Properties of Portfolio Weights, Working Paper Europa Universität, Frankfurt (Oder).Google Scholar
  18. Press, James S. (1972), Applied Multivariate Analysis, New York: Holt, Rinehart and Winston.Google Scholar
  19. White, Halbert (1980), A Heteroskedasticity-Consistent Covariance Matrix Estimator and Direct Test for Heteroskedasticity, Econometrica 48, 817–838.CrossRefGoogle Scholar

Copyright information

© Schmalenbach-Gesellschaft für Betriebswirtschaft e.V. (SG) 2006

Authors and Affiliations

  1. 1.Department of Finance, and Centre for Financial Research Cologne (CFR)University of CologneCologneGermany

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